整合影像信息学用于癌症研究：神经肿瘤学工作流自动化（I3CR-WANO）
Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO)
AI总结：
针对神经肿瘤学MRI数据异质性导致的人工整理负担，提出I3CR-WANO端到端框架，集成序列分类、预处理、分割和特征提取，在回顾性数据集上实现高准确率，助力大规模数据策展。
摘要：
利用日益增长的临床影像数据生成肿瘤评估的努力，由于数据异质性，仍然需要大量的人工数据整理工作。在此，我们提出了一种基于人工智能的解决方案，用于多序列神经肿瘤学MRI数据的聚合和处理，以提取定量肿瘤测量值。我们的端到端框架：i）使用集成分类器对MRI序列进行分类，ii）以可重复的方式预处理数据，iii）使用卷积神经网络描绘肿瘤组织亚型，iv）提取多样的影像组学特征。此外，该框架对缺失序列具有鲁棒性，并采用专家参与循环的方法，其中分割结果可由放射科医生手动修正。在Docker容器中实施该框架后，将其应用于两个回顾性胶质瘤数据集，这些数据集来自华盛顿大学医学院（WUSM；n=384）和M.D.安德森癌症中心（MDA；n=30），包含经病理确诊的胶质瘤患者的术前MRI扫描。扫描类型分类器达到了超过99%的准确率，分别正确识别了WUSM和MDA数据集中380/384和30/30个会话的序列。分割性能通过预测肿瘤掩膜与专家修正掩膜之间的Dice相似系数进行量化。对于WUSM和MDA，全肿瘤分割的平均Dice分数分别为0.882（±0.244）和0.977（±0.04）。该简化框架自动策展、处理和分割不同级别胶质瘤患者的原始MRI数据，使得大规模神经肿瘤学数据集的策展成为可能，并展示了作为临床实践辅助工具整合的高潜力。
英文摘要：
Efforts to utilize growing volumes of clinical imaging data to generate tumor evaluations continue to require significant manual data wrangling owing to the data heterogeneity. Here, we propose an artificial intelligence-based solution for the aggregation and processing of multisequence neuro-oncology MRI data to extract quantitative tumor measurements. Our end-to-end framework i) classifies MRI sequences using an ensemble classifier, ii) preprocesses the data in a reproducible manner, iii) delineates tumor tissue subtypes using convolutional neural networks, and iv) extracts diverse radiomic features. Moreover, it is robust to missing sequences and adopts an expert-in-the-loop approach, where the segmentation results may be manually refined by radiologists. Following the implementation of the framework in Docker containers, it was applied to two retrospective glioma datasets collected from the Washington University School of Medicine (WUSM; n = 384) and the M.D. Anderson Cancer Center (MDA; n = 30) comprising preoperative MRI scans from patients with pathologically confirmed gliomas. The scan-type classifier yielded an accuracy of over 99%, correctly identifying sequences from 380/384 and 30/30 sessions from the WUSM and MDA datasets, respectively. Segmentation performance was quantified using the Dice Similarity Coefficient between the predicted and expert-refined tumor masks. Mean Dice scores were 0.882 ($\pm$0.244) and 0.977 ($\pm$0.04) for whole tumor segmentation for WUSM and MDA, respectively. This streamlined framework automatically curated, processed, and segmented raw MRI data of patients with varying grades of gliomas, enabling the curation of large-scale neuro-oncology datasets and demonstrating a high potential for integration as an assistive tool in clinical practice.
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